Citations

Compared with two-dimensional MR elastography (MRE), three-dimensional MRE demonstrated better test-retest repeatability, between–field-strength reproducibility, and between-day reproducibility at 3 T for measurement of liver stiffness in adults with severe obesity but failed more frequently.

ABSTRACT Objective To evaluate intracochlear electrocochleography (ECochG) amplitude parameters during cochlear implantation (CI) using a novel multi‐frequency ECochG algorithm. Methods A multi‐institutional, prospective cohort study was performed at 18 high‐volume CI centers. The inclusion criteria were adults with sensorineural hearing loss and audiometric thresholds of ≤ 90 dB hearing level at 500 Hz undergoing CI with Advanced Bionics (Valencia, CA) Ultra 3D devices between 2024 and 2025. ECochG recordings were performed with simultaneous multi‐frequency stimulation of four frequencies between 125 and 4000 Hz during cochlear implant insertion. Concurrent multi‐frequency recording allowed extraction of amplitude and phase of each frequency individually. Post hoc analysis was performed to determine the difference in the number of drop alarms between single‐ and multi‐frequency ECochG. An ECochG amplitude drop of 6 dB was defined as a drop alarm. Insertion track patterns were compared between single‐ and multi‐frequency ECochG. Results One hundred ninety‐five ears were included. Mean number of drop alarms for the single‐frequency algorithm was 1.72 (95% CI: 1.52, 1.92; median 1) compared to 0.42 (95% CI: 0.31, 0.53; median 0) for multi‐frequency; p < 0.001. The number of Type C patterns (rise in amplitude during insertion followed by a drop) decreased with the multi‐frequency ECochG algorithm compared to the single‐frequency ECochG algorithm. The number of Type D patterns (no‐response) decreased, indicating that multi‐frequency ECochG generated more responses across the cochlea than single‐frequency ECochG. Conclusions A novel multi‐frequency ECochG algorithm during CI is associated with fewer drop alarms and altered insertion track patterns, which may provide a more accurate assessment of the cochlear microenvironment. Level of Evidence 3.

Abstract Neurosurgical and neuromodulation therapies such as deep brain stimulation (DBS) require millimeter-level accuracy to effectively target functional brain regions. Yet, many neuroanatomical boundaries remain invisible to current imaging and electrophysiology methods, limiting precision and contributing to suboptimal patient outcomes. Here, we introduce a self-supervised artificial intelligence (AI) framework that learns to delineate functional subregions directly from the spectral content of intraoperative local field potential (LFP) recordings, without the need for predefined biomarkers or anatomical labels. The framework identifies physiologic structure across the full spectrum of the signal and, through explainable AI (XAI), reveals the specific frequency components underlying these distinctions. Validated in the subthalamic nucleus (STN), the model aligned with clinically defined borders and rediscovered known beta oscillations. Applied to the motor thalamus in tremor patients, it consistently identified functional transitions corresponding to the ventral oralis posterior (Vop) and ventral intermediate (Vim) nuclei—regions where conventional methods fail to provide reliable boundaries. To assess clinical relevance, physiologically defined clusters were functionally evaluated using monopolar review data at their first DBS clinic postoperative visit, demonstrating distinct stimulation-response profiles across clusters and linking electrophysiologic segmentation to clinically meaningful programming outcomes. These findings demonstrate that intraoperative LFP recordings can be transformed into both a real-time guidance resource and a data-driven platform for biomarker discovery, establishing a foundation for more precise, individualized neuromodulation therapies and advancing our understanding of functional brain organization.

Background Grade group 5 (GG5) prostate cancer (PCa) carries a less favorable prognosis after standard-of-care (SOC) therapy, necessitating novel therapeutic approaches. High-dose rate brachytherapy (HDRBT) and androgen deprivation therapy (ADT) may modulate immune response in GG5 PCa, particularly in tumors with increased immune content. This study evaluated whether the addition of nivolumab to SOC was associated with improved disease control in patients with high-volume GG5 PCa, including those with oligometastatic disease. Methods In this non-randomized phase II trial, 31 patients with localized or oligometastatic GG5 PCa and >30% positive biopsy cores were evaluated between September 2018 and April 2021. Patients received four doses of nivolumab (240 mg every 2 weeks) beginning 4 weeks prior to HDRBT, alongside ADT, HDRBT, and external beam radiation. The primary endpoint was to evaluate whether the 2-year freedom from biochemical recurrence (FFBR) rate would exceed a prespecified historical control rate of 75%. Results Among the 31 patients, the median follow-up was 38.8 months (IQR 31.0–46.5 months). The addition of nivolumab to SOC RT with ADT was associated with a 2-year FFBR rate of 90.3% (95% CI 74.3% to 98.0%) (median FFBR not reached), exceeding the prespecified historical control rate of 75% (one-sided p value from binomial test=0.024). Definitive and probable nivolumab-related toxicity included 6.3% acute grade 2 and 6.3% acute grade 3 adverse events (AEs), with no grade 4+ AEs observed. A higher Decipher immunosuppression score at diagnosis correlated with early pathologic response (p=0.005) and was independently associated with time to metastatic failure (p=0.044). Conclusions Nivolumab combined with SOC was associated with encouraging FFBR in this high-risk GG5 PCa population and may represent a promising therapeutic intensification strategy. The Decipher immunosuppression score may serve as a predictive biomarker for response. These findings warrant further investigation in randomized trials.

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Abstract Cerebellopontine angle (CPA) tumors, including vestibular schwannomas (VS), often require microsurgical resection. These are typically surgical procedures performed by co-surgeons, and some controversy and confusion exist regarding use of common procedural terminology (CPT) codes for billing, given the American Medical Association (AMA) describes two distinct coding sets for such cases. This investigation endeavored to: (1) Comprehensively review all surgical coding options; (2) instruct on application; and (3) explore contemporary coding trends among surgeons. A multi-disciplinary task force (otologist/neurotologists and neurosurgeons) performed direct correspondence with various surgical societies, private consultancies, and the AMA. Survey instruments were distributed to active surgeons. Investigational period: 2022 to 2025. Survey instruments distributed at the 2023 American Neurotology Society (ANS) Fall Meeting. ANS, American Academy of Otolaryngology (AAO-HNSF), and the North American Skull Base Society (NASBS) representatives. Usage rate for CPT code sets (percentage of responders). Two CPT coding systems are currently accepted for surgical management of CPA tumors. The traditional/legacy codes (Indicator-2) allow use of co-surgery modifiers (-62). The newer approach/resection codes (skull base codes; Indicator-1) restrict modifier use. The history, application, and controversies surrounding the codes are detailed herein. About 40% of surgeons reported use of the traditional codes, while 45 to 49% use the skull base codes. Response rate was 31% of the active ANS membership in 2023. The AMA recognizes two CPT code sets for surgical management of CPA tumors. Both are appropriate, if properly applied. Contemporary surgeons likely utilize the sets in equal proportions.

Abstract Background Presacral tumours are rare and heterogeneous lesions arising within a complex anatomical space, resulting in variability in diagnosis and management. Increasing incidental detection and evolving surgical and oncological strategies have highlighted the need for contemporary guidance. The aim of this study was to establish an international expert consensus on the optimal diagnosis and management of presacral tumours. Methods An international panel of colorectal surgeons from high-volume units was convened. Evidence- and practice-based statements were developed after domain-based literature reviews and refined through five online and two in-person meetings. Consensus was achieved using a modified Delphi process with anonymous voting. Results In total, 16 colorectal surgeons participated in the study. Ten domains were identified resulting in the development of 22 statements based on expert opinion and retrospective studies. After two rounds, unanimous (100%) agreement was achieved for all statements. High-resolution MRI with structured synoptic reporting is endorsed as central to risk stratification. Selective image-guided transperineal or parasacral biopsy is recommended for high-risk lesions where histology would alter management. Complete surgical resection is defined as the operative goal, with approach tailored to tumour biology and anatomy; minimally invasive surgery is considered appropriate in selected cases. Non-operative surveillance is supported for carefully selected asymptomatic, low-risk cystic lesions. Postoperative surveillance is recommended in a risk-adapted manner according to histopathology and margin status. Conclusion This international consensus provides a pragmatic, risk-stratified framework for the multidisciplinary diagnosis and management of presacral tumours. These recommendations aim to reduce practice variation, support decision-making in specialized centres, and inform future prospective collaborative research.

Abstract Background : Traumatic encephalopathy syndrome (TES) is a clinical research construct used to identify individuals at risk for chronic traumatic encephalopathy (CTE) following exposure to repetitive head impacts (RHI). Adjudication of TES relies on clinical features such as progressive cognitive impairment and neurobehavioral dysregulation. Blood-based biomarkers and structural neuroimaging abnormalities have been associated with TES but are not part of the criteria. This study evaluated whether TES identification was associated with the combined contribution of cognitive performance, blood biomarkers, and structural neuroimaging measures across two well-characterized cohorts. Methods : Participants included 158 professional fighters from the Professional Athletes Brain Health Study and 149 former American football players from The DIAGNOSE CTE Research Project. Three indices were constructed representing complementary domains: a cognitive index reflecting cohort-specific cognitive features, a blood biomarker index including plasma neurofilament light chain, glial fibrillary acidic protein, total tau, tau phosphorylated at amino acid 231, and APOE -ε4 carrier status, and an imaging index comprising volumetric MRI measures of subcortical structures, ventricles, and corpus callosum subregions. Grouped weighted quantile sum regression models were estimated within each cohort to evaluate associations between these indices and TES while adjusting for age, race, competition status, and RHI exposure. Results : Multidomain models demonstrated improved model performance compared with single-domain models in both cohorts (PABHS: AUC=0.91, PPV=0.80; DIAGNOSE CTE: AUC=0.84, PPV=0.85). Biomarker and imaging indices contributed additional information across cohorts, although imaging contributions were more prominent in fighters whereas blood biomarker associations were stronger in football players. Conclusion : TES in RHI-exposed athletes was associated with a convergent clinicobiological profile observed across two independent cohorts with distinct exposure patterns. These findings support multidomain analytic frameworks for evaluating correlated biological signals in RHI-exposed populations and may inform future studies of TES and CTE.